Details

Title

Grid Fault Diagnosis Based on Information Entropy and Multi-source Information Fusion

Journal title

International Journal of Electronics and Telecommunications

Yearbook

2021

Volume

vol. 67

Issue

No 2

Affiliation

Zeng, Xin : School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, China ; Zeng, Xin : Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, China ; Xiong, Xingzhong : School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, China ; Xiong, Xingzhong : Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan Universityof Science and Engineering, Yibin, China ; Luo, Zhongqiang : School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, China ; Luo, Zhongqiang : Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan Universityof Science and Engineering, Yibin, China

Authors

Keywords

Information entropy ; Bayesian network ; Multisource information fusion ; D-S evidence theory ; fault diagnosis

Divisions of PAS

Nauki Techniczne

Coverage

143-148

Publisher

Polish Academy of Sciences Committee of Electronics and Telecommunications

Bibliography

[1] Yao Yuantao, Wang Jin, Xie Min, Hu Liqin and Wang Jianye, ”A new approach for fault diagnosis with full-scope simulator based on state information imaging in nuclear power plant”, Annals of Nuclear Energy, 2020, 141, 1-9.
[2] Lei Koua, Chuang Liua, Guo-wei Caia, Zhe Zhangb, ”Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression”, Electric Power Systems Research, 2020, 185, 1-9.
[3] Haibo Zhang, Kai Jia, Weijin Shi, Jianzhao Guo, Weizhi Su and Li Zhang, ”Power Grid Fault Diagnosis Based on Information Theory and Expert System”, Proceedings of the CSU-EPSA,, 2017, 29(8), 111-118.
[4] Jianfeng Zhou, Genserik Reniers and Laobing Zhang, ”A weighted fuzzy Petri-net based approach for security risk assessment in the chemical industry”, Chemical Engineering Science, 2017, 174, 136-145.
[5] Sen Wang and Xiaorun Li, ”Circuit Breaker Fault Detection Method Based on Bayesian Approach”, Industrial Control Computer, 2018, 31(4), 147-151.
[6] Kaikai Gu and Jiang Guo, ”Transformer Fault Diagnosis Method Based on Compact Fusion of Fuzzy Set and Fault Tree”, High Voltage Engineering , 2014, 40(05), 1507-1513.
[7] Jun Miao, Qikun Yuan, Liwen Liu, Zhipeng You and Zhang Wang, ”Research on robot circuit fault detection method based on dynamic Bayesian network”, Electronic Design Engineering, 2020, 28(9), 184- 188.
[8] Bangcheng Lai and Genxiu Wu, ”The Evidence Combination Method Based on Information Entropy”, Journal of Jiangxi Normal University (Natural Science), 2012, 36(5), 519-523.
[9] Libo Liu, Tingting Zhao, Yancang Li and Bin Wang, ”An Improved Whale Algorithm Based on Information Entry”, Mathematics in practice and theory, 2020, 50(2), 211-219.
[10] Juan Yan, Minfang Peng, et al., ”Fault Diagnosis of Grounding Grids Based on Information Entropy and Evidence Fusion”, Proceedings of the CSU-EPSA, 2017, 29(12),8-13.
[11] Ershadi, Mohammad Mahdi and Seifi, Abbas, ”An efficient Bayesian network for differential diagnosis using experts’ knowledge”, International Journal of Intelligent Computing and Cybernetics, 2020, 13(1), 103-126.
[12] Guan Li, Zhifeng Liu, Ligang Cai and Jun Yan, ”Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory”, Sensors, 2020, 20(4), 1- 17.
[13] Xiaofei He, Xiaoyang Tong and Shu Zhou, ”Power system fault diagnosis based on Bayesian network and fault section location”, Power system protection and control, 2010, 38(12), 29-34.

Date

2021.05.25

Type

Article

Identifier

DOI: 10.24425/ijet.2021.135956

Source

International Journal of Electronics and Telecommunications; 2021; vol. 67; No 2; 143-148
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